Import model objects

load('/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Data/GER/ger_list_results_fixed_window.RData')
load('/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Data/US/us_list_results_fixed_window.RData')

library(lmtest)
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
library(car)
## Loading required package: carData
library(survival)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.0     ✓ purrr   0.3.3
## ✓ tibble  3.0.0     ✓ dplyr   0.8.5
## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## Warning: package 'tibble' was built under R version 3.6.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
## x dplyr::recode() masks car::recode()
## x purrr::some()   masks car::some()
ger_list_results$ger_lm_prev_slope$pers_o$lm_all %>% resid() %>% ks.test(y=pnorm)
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  .
## D = 0.12717, p-value = 6.229e-06
## alternative hypothesis: two-sided
us_list_results$us_lm_prev_slope$pers_o$lm_all %>% resid() %>% ks.test(y=pnorm)
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  .
## D = 0.15739, p-value < 2.2e-16
## alternative hypothesis: two-sided
ger_list_results$ger_lm_prev_slope$pers_o$lm_base %>% bptest()
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.20014, df = 1, p-value = 0.6546

Define Functions

list_iterater <- function(models, test) {
  for(i in models){
    for(j in i){
      
      if(test == 'qq'){j %>% plot(2)}
      if(test == 'ks'){j %>% resid() %>% ks.test(y=pnorm) %>% print()}
      if(test == 'bp'){j %>% bptest() %>% print()}
      if(test == 'ph'){j %>% cox.zph() %>% print()}
    }
  }
}

Assumptions GER COVID-19 onsets

list_iterater(ger_list_results$ger_cox_prev_onset, test = 'ph')
##        chisq df     p
## pers    6.21  1 0.013
## GLOBAL  6.21  1 0.013
##               chisq df       p
## pers          4.007  1   0.045
## age          21.844  1 3.0e-06
## male          0.454  1   0.500
## conservative 15.769  1 7.2e-05
## GLOBAL       25.231  4 4.5e-05
##             chisq df    p
## pers      1.30051  1 0.25
## academics 0.10832  1 0.74
## medinc    0.00668  1 0.93
## manufact  0.07463  1 0.78
## GLOBAL    1.38751  4 0.85
##              chisq df      p
## pers          6.89  1 0.0087
## airport_dist  7.94  1 0.0048
## tourism       1.65  1 0.1994
## healthcare    6.31  1 0.0120
## popdens       6.31  1 0.0120
## GLOBAL       17.20  5 0.0041
##                 chisq df       p
## pers          1.05049  1 0.30539
## age          11.81505  1 0.00059
## male          4.76514  1 0.02904
## conservative  4.75085  1 0.02928
## academics     0.11884  1 0.73030
## medinc        2.27744  1 0.13127
## manufact      0.49479  1 0.48180
## airport_dist  0.69603  1 0.40412
## tourism       0.07452  1 0.78486
## healthcare    0.00138  1 0.97032
## popdens       1.36622  1 0.24246
## GLOBAL       17.21722 11 0.10161
##        chisq df    p
## pers    2.51  1 0.11
## GLOBAL  2.51  1 0.11
##               chisq df       p
## pers          7.888  1 0.00498
## age          19.642  1 9.3e-06
## male          0.658  1 0.41721
## conservative 14.163  1 0.00017
## GLOBAL       23.934  4 8.2e-05
##            chisq df    p
## pers      1.6479  1 0.20
## academics 0.0923  1 0.76
## medinc    0.0737  1 0.79
## manufact  0.0601  1 0.81
## GLOBAL    1.9272  4 0.75
##              chisq df      p
## pers          3.23  1 0.0723
## airport_dist  8.82  1 0.0030
## tourism       1.55  1 0.2126
## healthcare    6.01  1 0.0142
## popdens       6.41  1 0.0114
## GLOBAL       16.26  5 0.0061
##                 chisq df       p
## pers         3.45e+00  1 0.06333
## age          1.28e+01  1 0.00034
## male         4.89e+00  1 0.02701
## conservative 5.00e+00  1 0.02530
## academics    8.68e-02  1 0.76827
## medinc       2.30e+00  1 0.12979
## manufact     6.41e-01  1 0.42342
## airport_dist 8.22e-01  1 0.36449
## tourism      6.81e-02  1 0.79418
## healthcare   9.25e-04  1 0.97574
## popdens      1.38e+00  1 0.23943
## GLOBAL       1.87e+01 11 0.06668
##        chisq df      p
## pers    10.2  1 0.0014
## GLOBAL  10.2  1 0.0014
##               chisq df       p
## pers          9.075  1  0.0026
## age          20.874  1 4.9e-06
## male          0.256  1  0.6128
## conservative 15.413  1 8.6e-05
## GLOBAL       29.120  4 7.4e-06
##            chisq df     p
## pers      3.3334  1 0.068
## academics 0.3435  1 0.558
## medinc    0.0519  1 0.820
## manufact  0.0950  1 0.758
## GLOBAL    3.3556  4 0.500
##              chisq df      p
## pers          8.30  1 0.0040
## airport_dist  7.50  1 0.0062
## tourism       1.70  1 0.1924
## healthcare    7.55  1 0.0060
## popdens       6.36  1 0.0117
## GLOBAL       21.03  5 0.0008
##                 chisq df       p
## pers         4.09e+00  1 0.04323
## age          1.17e+01  1 0.00064
## male         4.38e+00  1 0.03633
## conservative 4.86e+00  1 0.02754
## academics    2.09e-01  1 0.64747
## medinc       2.49e+00  1 0.11445
## manufact     4.16e-01  1 0.51913
## airport_dist 7.69e-01  1 0.38049
## tourism      8.12e-02  1 0.77565
## healthcare   5.23e-04  1 0.98176
## popdens      1.31e+00  1 0.25286
## GLOBAL       1.83e+01 11 0.07556
##        chisq df      p
## pers    7.14  1 0.0076
## GLOBAL  7.14  1 0.0076
##              chisq df       p
## pers          3.46  1  0.0628
## age          21.13  1 4.3e-06
## male          0.44  1  0.5072
## conservative 15.05  1  0.0001
## GLOBAL       28.94  4 8.1e-06
##            chisq df     p
## pers      2.9859  1 0.084
## academics 0.0492  1 0.825
## medinc    0.0130  1 0.909
## manufact  0.0662  1 0.797
## GLOBAL    3.1061  4 0.540
##              chisq df       p
## pers          7.98  1 0.00474
## airport_dist  8.47  1 0.00362
## tourism       1.55  1 0.21352
## healthcare    5.87  1 0.01537
## popdens       6.24  1 0.01247
## GLOBAL       20.74  5 0.00091
##                 chisq df      p
## pers          2.34952  1 0.1253
## age          12.10476  1 0.0005
## male          5.08073  1 0.0242
## conservative  4.84600  1 0.0277
## academics     0.03360  1 0.8546
## medinc        2.16525  1 0.1412
## manufact      0.63890  1 0.4241
## airport_dist  0.64944  1 0.4203
## tourism       0.06193  1 0.8035
## healthcare    0.00924  1 0.9234
## popdens       1.17671  1 0.2780
## GLOBAL       21.93744 11 0.0249
##        chisq df     p
## pers    5.91  1 0.015
## GLOBAL  5.91  1 0.015
##               chisq df       p
## pers          4.570  1   0.033
## age          19.880  1 8.2e-06
## male          0.332  1   0.564
## conservative 15.516  1 8.2e-05
## GLOBAL       26.962  4 2.0e-05
##           chisq df    p
## pers      0.507  1 0.48
## academics 0.202  1 0.65
## medinc    0.128  1 0.72
## manufact  0.280  1 0.60
## GLOBAL    1.003  4 0.91
##              chisq df       p
## pers          5.90  1 0.01512
## airport_dist  7.85  1 0.00509
## tourism       1.40  1 0.23743
## healthcare    5.39  1 0.02023
## popdens       7.23  1 0.00716
## GLOBAL       20.61  5 0.00096
##                chisq df       p
## pers          0.7484  1 0.38699
## age          10.8876  1 0.00097
## male          4.0270  1 0.04478
## conservative  5.2602  1 0.02182
## academics     0.2251  1 0.63517
## medinc        1.1519  1 0.28314
## manufact      0.1375  1 0.71080
## airport_dist  0.8884  1 0.34590
## tourism       0.0349  1 0.85181
## healthcare    0.0493  1 0.82426
## popdens       1.4904  1 0.22216
## GLOBAL       15.5926 11 0.15694

Assumptions US COVID-19 onsets

list_iterater(us_list_results$us_cox_prev_onset, test = 'ph')
##        chisq df      p
## pers     133  1 <2e-16
## GLOBAL   133  1 <2e-16
##                chisq df       p
## pers         106.580  1 < 2e-16
## age            0.453  1     0.5
## male          21.391  1 3.7e-06
## conservative 102.647  1 < 2e-16
## GLOBAL       172.622  4 < 2e-16
##           chisq df       p
## pers       96.8  1 < 2e-16
## academics 144.2  1 < 2e-16
## medinc     47.3  1 5.9e-12
## manufact   61.3  1 5.0e-15
## GLOBAL    182.7  4 < 2e-16
##               chisq df       p
## pers          92.07  1 < 2e-16
## airport_dist   8.83  1   0.003
## tourism       15.46  1 8.4e-05
## healthcare    37.50  1 9.1e-10
## popdens        3.87  1   0.049
## GLOBAL       122.81  5 < 2e-16
##                 chisq df       p
## pers          71.8062  1 < 2e-16
## age            0.0164  1 0.89806
## male          21.4651  1 3.6e-06
## conservative  70.0537  1 < 2e-16
## academics    110.6622  1 < 2e-16
## medinc        32.3876  1 1.3e-08
## manufact      46.1464  1 1.1e-11
## airport_dist   2.0257  1 0.15466
## tourism       13.2975  1 0.00027
## healthcare    25.6451  1 4.1e-07
## popdens       32.9347  1 9.5e-09
## GLOBAL       173.6175 11 < 2e-16
##        chisq df    p
## pers   0.729  1 0.39
## GLOBAL 0.729  1 0.39
##               chisq df       p
## pers           2.56  1    0.11
## age            1.30  1    0.25
## male          20.65  1 5.5e-06
## conservative 107.90  1 < 2e-16
## GLOBAL       124.50  4 < 2e-16
##             chisq df       p
## pers        0.112  1    0.74
## academics 153.781  1 < 2e-16
## medinc     53.480  1 2.6e-13
## manufact   64.290  1 1.1e-15
## GLOBAL    170.151  4 < 2e-16
##               chisq df       p
## pers          0.425  1  0.5143
## airport_dist  7.913  1  0.0049
## tourism      15.657  1 7.6e-05
## healthcare   41.027  1 1.5e-10
## popdens       1.702  1  0.1921
## GLOBAL       59.516  5 1.5e-11
##                 chisq df       p
## pers         2.14e-01  1 0.64358
## age          4.78e-03  1 0.94491
## male         1.97e+01  1 9.2e-06
## conservative 7.15e+01  1 < 2e-16
## academics    1.17e+02  1 < 2e-16
## medinc       3.90e+01  1 4.2e-10
## manufact     4.88e+01  1 2.8e-12
## airport_dist 1.27e+00  1 0.26002
## tourism      1.39e+01  1 0.00019
## healthcare   2.87e+01  1 8.3e-08
## popdens      3.04e+01  1 3.5e-08
## GLOBAL       1.71e+02 11 < 2e-16
##        chisq df    p
## pers    1.91  1 0.17
## GLOBAL  1.91  1 0.17
##                chisq df       p
## pers           0.913  1    0.34
## age            1.251  1    0.26
## male          22.838  1 1.8e-06
## conservative 111.394  1 < 2e-16
## GLOBAL       124.947  4 < 2e-16
##             chisq df       p
## pers        0.964  1    0.33
## academics 148.095  1 < 2e-16
## medinc     51.959  1 5.7e-13
## manufact   62.359  1 2.9e-15
## GLOBAL    162.064  4 < 2e-16
##               chisq df       p
## pers          0.831  1  0.3619
## airport_dist  8.887  1  0.0029
## tourism      13.795  1  0.0002
## healthcare   39.805  1 2.8e-10
## popdens       3.149  1  0.0760
## GLOBAL       58.645  5 2.3e-11
##                 chisq df       p
## pers         2.52e-01  1 0.61546
## age          4.88e-03  1 0.94429
## male         2.27e+01  1 1.9e-06
## conservative 7.45e+01  1 < 2e-16
## academics    1.14e+02  1 < 2e-16
## medinc       3.65e+01  1 1.6e-09
## manufact     4.77e+01  1 4.9e-12
## airport_dist 1.94e+00  1 0.16421
## tourism      1.26e+01  1 0.00038
## healthcare   2.73e+01  1 1.7e-07
## popdens      3.49e+01  1 3.6e-09
## GLOBAL       1.69e+02 11 < 2e-16
##        chisq df    p
## pers   0.402  1 0.53
## GLOBAL 0.402  1 0.53
##               chisq df       p
## pers           1.03  1    0.31
## age            1.37  1    0.24
## male          21.87  1 2.9e-06
## conservative 111.50  1 < 2e-16
## GLOBAL       134.61  4 < 2e-16
##             chisq df       p
## pers        0.603  1    0.44
## academics 153.090  1 < 2e-16
## medinc     54.952  1 1.2e-13
## manufact   68.508  1 < 2e-16
## GLOBAL    172.867  4 < 2e-16
##                chisq df       p
## pers          0.0332  1  0.8554
## airport_dist  8.7075  1  0.0032
## tourism      15.3470  1 8.9e-05
## healthcare   42.5992  1 6.7e-11
## popdens       3.5426  1  0.0598
## GLOBAL       61.8373  5 5.1e-12
##                 chisq df       p
## pers         3.81e-03  1 0.95076
## age          1.66e-04  1 0.98973
## male         2.11e+01  1 4.4e-06
## conservative 7.67e+01  1 < 2e-16
## academics    1.19e+02  1 < 2e-16
## medinc       3.83e+01  1 6.1e-10
## manufact     5.16e+01  1 6.7e-13
## airport_dist 1.77e+00  1 0.18394
## tourism      1.36e+01  1 0.00023
## healthcare   3.01e+01  1 4.2e-08
## popdens      3.77e+01  1 8.1e-10
## GLOBAL       1.76e+02 11 < 2e-16
##        chisq df       p
## pers    49.7  1 1.8e-12
## GLOBAL  49.7  1 1.8e-12
##                chisq df       p
## pers          35.610  1 2.4e-09
## age            0.744  1    0.39
## male          21.775  1 3.1e-06
## conservative 109.483  1 < 2e-16
## GLOBAL       133.700  4 < 2e-16
##           chisq df       p
## pers       47.0  1 7.2e-12
## academics 158.3  1 < 2e-16
## medinc     58.3  1 2.2e-14
## manufact   62.3  1 2.9e-15
## GLOBAL    177.2  4 < 2e-16
##              chisq df       p
## pers         33.48  1 7.2e-09
## airport_dist  6.46  1 0.01102
## tourism      12.01  1 0.00053
## healthcare   41.31  1 1.3e-10
## popdens       3.34  1 0.06744
## GLOBAL       81.47  5 4.1e-16
##                 chisq df       p
## pers          25.1056  1 5.4e-07
## age            0.0324  1 0.85712
## male          21.4504  1 3.6e-06
## conservative  71.5756  1 < 2e-16
## academics    119.2835  1 < 2e-16
## medinc        40.8184  1 1.7e-10
## manufact      47.3892  1 5.8e-12
## airport_dist   1.6077  1 0.20481
## tourism       11.5992  1 0.00066
## healthcare    28.6560  1 8.6e-08
## popdens       30.7441  1 2.9e-08
## GLOBAL       176.2965 11 < 2e-16

Assumptions GER COVID-19 growth rates

list_iterater(ger_list_results$ger_lm_prev_slope, test = 'qq')

list_iterater(ger_list_results$ger_lm_prev_slope, test = 'bp')
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.20014, df = 1, p-value = 0.6546
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 1.7565, df = 4, p-value = 0.7804
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 8.8926, df = 4, p-value = 0.06384
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 12.765, df = 5, p-value = 0.02568
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 19.536, df = 11, p-value = 0.05213
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.014025, df = 1, p-value = 0.9057
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 2.0882, df = 4, p-value = 0.7195
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 8.2989, df = 4, p-value = 0.08122
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 10.635, df = 5, p-value = 0.05912
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 18.827, df = 11, p-value = 0.06426
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.9348, df = 1, p-value = 0.3336
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 2.8075, df = 4, p-value = 0.5905
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 8.2707, df = 4, p-value = 0.08215
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 10.975, df = 5, p-value = 0.05188
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 18.978, df = 11, p-value = 0.06149
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 1.9621, df = 1, p-value = 0.1613
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 2.3503, df = 4, p-value = 0.6716
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 8.6682, df = 4, p-value = 0.06995
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 17.72, df = 5, p-value = 0.003318
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 19.885, df = 11, p-value = 0.04694
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.77295, df = 1, p-value = 0.3793
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 2.6459, df = 4, p-value = 0.6187
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 9.1115, df = 4, p-value = 0.05837
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 11.656, df = 5, p-value = 0.03981
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 19.53, df = 11, p-value = 0.05222

Assumptions US COVID-19 growth rates

list_iterater(us_list_results$us_lm_prev_slope, test = 'qq')

list_iterater(us_list_results$us_lm_prev_slope, test = 'bp')
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 25.881, df = 1, p-value = 3.63e-07
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 58.996, df = 4, p-value = 4.715e-12
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 27.503, df = 4, p-value = 1.573e-05
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 88.757, df = 5, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 121.41, df = 11, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.45532, df = 1, p-value = 0.4998
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 58.951, df = 4, p-value = 4.819e-12
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 35.857, df = 4, p-value = 3.096e-07
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 93.365, df = 5, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 127.65, df = 11, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 3.7171, df = 1, p-value = 0.05386
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 59.824, df = 4, p-value = 3.158e-12
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 21.132, df = 4, p-value = 0.0002982
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 84.501, df = 5, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 119.57, df = 11, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 7.0583, df = 1, p-value = 0.00789
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 65.59, df = 4, p-value = 1.933e-13
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 57.311, df = 4, p-value = 1.064e-11
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 108.23, df = 5, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 147.26, df = 11, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 12.544, df = 1, p-value = 0.0003975
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 58.66, df = 4, p-value = 5.545e-12
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 31.917, df = 4, p-value = 1.989e-06
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 94.189, df = 5, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 123.74, df = 11, p-value < 2.2e-16

Assumptions GER socdist onsets

list_iterater(ger_list_results$ger_cox_socdist_cpt, test = 'ph')
##        chisq df    p
## pers   0.187  1 0.67
## GLOBAL 0.187  1 0.67
##              chisq df     p
## pers         0.589  1 0.443
## age          1.510  1 0.219
## male         5.639  1 0.018
## conservative 1.333  1 0.248
## GLOBAL       8.287  4 0.082
##             chisq df     p
## pers      0.07128  1 0.789
## academics 0.00457  1 0.946
## medinc    6.30499  1 0.012
## manufact  5.40654  1 0.020
## GLOBAL    9.07479  4 0.059
##               chisq df     p
## pers         0.0093  1 0.923
## airport_dist 3.7655  1 0.052
## tourism      0.4039  1 0.525
## healthcare   0.9540  1 0.329
## popdens      0.0948  1 0.758
## GLOBAL       5.0000  5 0.416
##                chisq df     p
## pers          0.0439  1 0.834
## age           3.8044  1 0.051
## male          6.2038  1 0.013
## conservative  2.3823  1 0.123
## academics     0.0481  1 0.826
## medinc        3.3040  1 0.069
## manufact      2.3737  1 0.123
## airport_dist  6.1123  1 0.013
## tourism       1.5087  1 0.219
## healthcare    1.5767  1 0.209
## popdens       0.0263  1 0.871
## onset_prev    2.8203  1 0.093
## slope_prev    4.1380  1 0.042
## GLOBAL       17.3136 13 0.185
##        chisq df    p
## pers   0.284  1 0.59
## GLOBAL 0.284  1 0.59
##              chisq df     p
## pers          1.15  1 0.284
## age           1.34  1 0.247
## male          5.15  1 0.023
## conservative  1.12  1 0.290
## GLOBAL        7.65  4 0.105
##            chisq df     p
## pers      0.5249  1 0.469
## academics 0.0037  1 0.952
## medinc    5.4471  1 0.020
## manufact  5.4390  1 0.020
## GLOBAL    7.8952  4 0.095
##               chisq df     p
## pers         0.1079  1 0.743
## airport_dist 4.6196  1 0.032
## tourism      0.4192  1 0.517
## healthcare   1.2591  1 0.262
## popdens      0.0509  1 0.822
## GLOBAL       6.7462  5 0.240
##                chisq df      p
## pers          0.5092  1 0.4755
## age           3.8442  1 0.0499
## male          5.7297  1 0.0167
## conservative  2.3451  1 0.1257
## academics     0.0958  1 0.7570
## medinc        3.2215  1 0.0727
## manufact      2.2607  1 0.1327
## airport_dist  6.8590  1 0.0088
## tourism       1.4968  1 0.2212
## healthcare    1.6372  1 0.2007
## popdens       0.0114  1 0.9150
## onset_prev    3.0881  1 0.0789
## slope_prev    4.5326  1 0.0333
## GLOBAL       17.6092 13 0.1729
##        chisq df    p
## pers    1.04  1 0.31
## GLOBAL  1.04  1 0.31
##              chisq df     p
## pers          1.52  1 0.218
## age           1.18  1 0.276
## male          4.82  1 0.028
## conservative  1.05  1 0.307
## GLOBAL        7.57  4 0.109
##            chisq df     p
## pers      0.9332  1 0.334
## academics 0.0254  1 0.873
## medinc    5.5409  1 0.019
## manufact  4.7740  1 0.029
## GLOBAL    8.6942  4 0.069
##              chisq df    p
## pers         1.232  1 0.27
## airport_dist 4.230  1 0.04
## tourism      0.475  1 0.49
## healthcare   1.037  1 0.31
## popdens      0.103  1 0.75
## GLOBAL       5.784  5 0.33
##                chisq df      p
## pers          0.8818  1 0.3477
## age           3.7021  1 0.0543
## male          5.4792  1 0.0192
## conservative  2.2946  1 0.1298
## academics     0.1120  1 0.7379
## medinc        2.5620  1 0.1095
## manufact      1.7891  1 0.1810
## airport_dist  7.1031  1 0.0077
## tourism       1.7503  1 0.1858
## healthcare    1.6702  1 0.1962
## popdens       0.0123  1 0.9115
## onset_prev    2.9767  1 0.0845
## slope_prev    4.3407  1 0.0372
## GLOBAL       17.9668 13 0.1588
##        chisq df    p
## pers   0.667  1 0.41
## GLOBAL 0.667  1 0.41
##              chisq df     p
## pers          1.50  1 0.221
## age           1.30  1 0.255
## male          4.96  1 0.026
## conservative  1.09  1 0.295
## GLOBAL        8.17  4 0.086
##             chisq df     p
## pers      0.96909  1 0.325
## academics 0.00695  1 0.934
## medinc    5.51116  1 0.019
## manufact  5.08746  1 0.024
## GLOBAL    7.82380  4 0.098
##               chisq df     p
## pers         0.4477  1 0.503
## airport_dist 4.4029  1 0.036
## tourism      0.4347  1 0.510
## healthcare   1.0666  1 0.302
## popdens      0.0851  1 0.770
## GLOBAL       5.8358  5 0.323
##                chisq df      p
## pers          1.3945  1 0.2377
## age           3.7995  1 0.0513
## male          5.6669  1 0.0173
## conservative  2.3341  1 0.1266
## academics     0.0935  1 0.7598
## medinc        3.2418  1 0.0718
## manufact      2.2310  1 0.1353
## airport_dist  6.8871  1 0.0087
## tourism       1.5300  1 0.2161
## healthcare    1.6230  1 0.2027
## popdens       0.0108  1 0.9173
## onset_prev    2.9882  1 0.0839
## slope_prev    4.5077  1 0.0337
## GLOBAL       17.5141 13 0.1769
##        chisq df    p
## pers    0.66  1 0.42
## GLOBAL  0.66  1 0.42
##              chisq df     p
## pers         0.268  1 0.605
## age          1.388  1 0.239
## male         4.860  1 0.027
## conservative 1.229  1 0.268
## GLOBAL       6.221  4 0.183
##             chisq df     p
## pers      0.91068  1 0.340
## academics 0.00965  1 0.922
## medinc    5.97668  1 0.014
## manufact  5.24467  1 0.022
## GLOBAL    7.79339  4 0.099
##               chisq df     p
## pers         0.6571  1 0.418
## airport_dist 4.0898  1 0.043
## tourism      0.4325  1 0.511
## healthcare   1.0549  1 0.304
## popdens      0.0785  1 0.779
## GLOBAL       5.3354  5 0.376
##                chisq df      p
## pers          0.5507  1 0.4580
## age           3.8479  1 0.0498
## male          5.6687  1 0.0173
## conservative  2.3884  1 0.1222
## academics     0.0979  1 0.7544
## medinc        3.2705  1 0.0705
## manufact      2.2318  1 0.1352
## airport_dist  6.7326  1 0.0095
## tourism       1.5199  1 0.2176
## healthcare    1.5803  1 0.2087
## popdens       0.0102  1 0.9194
## onset_prev    3.0841  1 0.0791
## slope_prev    4.5356  1 0.0332
## GLOBAL       18.3991 13 0.1429

Assumptions US socdist onsets

list_iterater(us_list_results$us_cox_socdist_cpt, test = 'ph')
##        chisq df       p
## pers    40.1  1 2.4e-10
## GLOBAL  40.1  1 2.4e-10
##              chisq df       p
## pers         40.37  1 2.1e-10
## age           3.15  1 0.07575
## male          8.88  1 0.00288
## conservative 12.21  1 0.00047
## GLOBAL       47.17  4 1.4e-09
##            chisq df       p
## pers      35.276  1 2.9e-09
## academics 10.776  1   0.001
## medinc     0.712  1   0.399
## manufact   6.477  1   0.011
## GLOBAL    37.669  4 1.3e-07
##               chisq df       p
## pers          45.88  1 1.3e-11
## airport_dist  33.83  1 6.0e-09
## tourism       25.00  1 5.7e-07
## healthcare     1.81  1    0.18
## popdens       47.05  1 6.9e-12
## GLOBAL       101.93  5 < 2e-16
##                chisq df       p
## pers          34.785  1 3.7e-09
## age            1.678  1  0.1953
## male           6.300  1  0.0121
## conservative  15.885  1 6.7e-05
## academics     10.398  1  0.0013
## medinc         0.941  1  0.3321
## manufact       7.793  1  0.0052
## airport_dist  29.969  1 4.4e-08
## tourism       19.239  1 1.2e-05
## healthcare     0.718  1  0.3968
## popdens       34.662  1 3.9e-09
## onset_prev    52.981  1 3.4e-13
## slope_prev    59.230  1 1.4e-14
## GLOBAL       121.454 13 < 2e-16
##        chisq df       p
## pers    12.4  1 0.00043
## GLOBAL  12.4  1 0.00043
##              chisq df       p
## pers         12.03  1 0.00052
## age           2.36  1 0.12437
## male          6.84  1 0.00890
## conservative 10.00  1 0.00157
## GLOBAL       26.40  4 2.6e-05
##            chisq df       p
## pers      10.018  1  0.0016
## academics  8.193  1  0.0042
## medinc     0.203  1  0.6524
## manufact   5.864  1  0.0155
## GLOBAL    26.466  4 2.5e-05
##               chisq df       p
## pers           8.91  1  0.0028
## airport_dist  35.75  1 2.2e-09
## tourism       24.41  1 7.8e-07
## healthcare     1.13  1  0.2872
## popdens       63.76  1 1.4e-15
## GLOBAL       112.08  5 < 2e-16
##                chisq df       p
## pers           8.922  1 0.00282
## age            1.096  1 0.29514
## male           5.384  1 0.02032
## conservative  14.733  1 0.00012
## academics      8.988  1 0.00272
## medinc         0.515  1 0.47281
## manufact       8.175  1 0.00425
## airport_dist  32.147  1 1.4e-08
## tourism       18.978  1 1.3e-05
## healthcare     0.414  1 0.51984
## popdens       36.422  1 1.6e-09
## onset_prev    49.691  1 1.8e-12
## slope_prev    59.684  1 1.1e-14
## GLOBAL       120.284 13 < 2e-16
##        chisq df    p
## pers   0.561  1 0.45
## GLOBAL 0.561  1 0.45
##               chisq df       p
## pers          0.427  1 0.51355
## age           2.983  1 0.08417
## male          7.640  1 0.00571
## conservative 10.147  1 0.00145
## GLOBAL       18.922  4 0.00081
##             chisq df      p
## pers       0.0957  1 0.7570
## academics  9.7760  1 0.0018
## medinc     0.5531  1 0.4571
## manufact   6.6345  1 0.0100
## GLOBAL    17.1795  4 0.0018
##                chisq df       p
## pers           0.402  1    0.53
## airport_dist  32.089  1 1.5e-08
## tourism       26.039  1 3.3e-07
## healthcare     1.454  1    0.23
## popdens       59.669  1 1.1e-14
## GLOBAL       102.636  5 < 2e-16
##                 chisq df       p
## pers         1.28e-03  1 0.97144
## age          1.39e+00  1 0.23869
## male         5.95e+00  1 0.01473
## conservative 1.48e+01  1 0.00012
## academics    1.03e+01  1 0.00133
## medinc       8.99e-01  1 0.34313
## manufact     8.21e+00  1 0.00417
## airport_dist 2.96e+01  1 5.2e-08
## tourism      1.96e+01  1 9.6e-06
## healthcare   5.16e-01  1 0.47244
## popdens      3.61e+01  1 1.9e-09
## onset_prev   5.21e+01  1 5.2e-13
## slope_prev   6.00e+01  1 9.3e-15
## GLOBAL       1.18e+02 13 < 2e-16
##        chisq df       p
## pers    16.4  1 5.1e-05
## GLOBAL  16.4  1 5.1e-05
##              chisq df       p
## pers         15.94  1 6.5e-05
## age           2.83  1  0.0925
## male          7.42  1  0.0065
## conservative 10.10  1  0.0015
## GLOBAL       28.85  4 8.4e-06
##            chisq df       p
## pers      13.905  1 0.00019
## academics  9.124  1 0.00252
## medinc     0.395  1 0.52976
## manufact   6.149  1 0.01315
## GLOBAL    32.382  4 1.6e-06
##              chisq df       p
## pers          11.5  1  0.0007
## airport_dist  33.8  1 6.0e-09
## tourism       24.3  1 8.3e-07
## healthcare     1.1  1  0.2943
## popdens       61.5  1 4.4e-15
## GLOBAL       116.8  5 < 2e-16
##                chisq df       p
## pers          11.805  1 0.00059
## age            1.281  1 0.25765
## male           5.737  1 0.01661
## conservative  14.695  1 0.00013
## academics      9.849  1 0.00170
## medinc         0.717  1 0.39707
## manufact       8.389  1 0.00378
## airport_dist  30.178  1 3.9e-08
## tourism       18.997  1 1.3e-05
## healthcare     0.393  1 0.53092
## popdens       35.993  1 2.0e-09
## onset_prev    51.526  1 7.1e-13
## slope_prev    60.946  1 5.9e-15
## GLOBAL       122.473 13 < 2e-16
##        chisq df      p
## pers    8.41  1 0.0037
## GLOBAL  8.41  1 0.0037
##              chisq df       p
## pers          8.65  1 0.00328
## age           1.99  1 0.15812
## male          7.00  1 0.00814
## conservative 10.05  1 0.00152
## GLOBAL       19.68  4 0.00058
##            chisq df       p
## pers       9.659  1 0.00188
## academics  7.704  1 0.00551
## medinc     0.124  1 0.72482
## manufact   7.032  1 0.00801
## GLOBAL    20.333  4 0.00043
##              chisq df       p
## pers          7.99  1  0.0047
## airport_dist 34.83  1 3.6e-09
## tourism      24.38  1 7.9e-07
## healthcare    0.57  1  0.4502
## popdens      51.22  1 8.2e-13
## GLOBAL       97.26  5 < 2e-16
##                chisq df       p
## pers           9.841  1  0.0017
## age            0.987  1  0.3204
## male           5.645  1  0.0175
## conservative  15.669  1 7.5e-05
## academics      9.134  1  0.0025
## medinc         0.439  1  0.5078
## manufact       9.371  1  0.0022
## airport_dist  30.646  1 3.1e-08
## tourism       19.652  1 9.3e-06
## healthcare     0.180  1  0.6712
## popdens       35.728  1 2.3e-09
## onset_prev    50.955  1 9.4e-13
## slope_prev    60.718  1 6.6e-15
## GLOBAL       122.278 13 < 2e-16

Assumptions GER socdist adjustment levels

list_iterater(ger_list_results$ger_lm_socdist_mean, test = 'qq')

list_iterater(ger_list_results$ger_lm_socdist_mean, test = 'bp')
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 6.6073, df = 1, p-value = 0.01016
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 11.402, df = 4, p-value = 0.02239
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 38.391, df = 4, p-value = 9.306e-08
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.91771, df = 5, p-value = 0.9689
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 36.57, df = 13, p-value = 0.0004836
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 2.8103, df = 1, p-value = 0.09366
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 16.321, df = 4, p-value = 0.002617
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 43.588, df = 4, p-value = 7.812e-09
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 3.9413, df = 5, p-value = 0.5579
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 40.982, df = 13, p-value = 9.594e-05
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 3.9066, df = 1, p-value = 0.0481
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 14.173, df = 4, p-value = 0.006764
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 37.668, df = 4, p-value = 1.312e-07
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 1.6777, df = 5, p-value = 0.8917
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 36.106, df = 13, p-value = 0.0005712
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 6.5055, df = 1, p-value = 0.01075
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 15.626, df = 4, p-value = 0.003565
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 40.792, df = 4, p-value = 2.968e-08
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 1.6497, df = 5, p-value = 0.8952
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 39.059, df = 13, p-value = 0.0001956
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 2.3883, df = 1, p-value = 0.1222
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 14.994, df = 4, p-value = 0.004713
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 38.685, df = 4, p-value = 8.091e-08
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 2.6498, df = 5, p-value = 0.7538
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 38.358, df = 13, p-value = 0.000253

Assumptions US socdist adjustment levels

list_iterater(us_list_results$us_lm_socdist_mean, test = 'qq')

list_iterater(us_list_results$us_lm_socdist_mean, test = 'bp')
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 11.773, df = 1, p-value = 0.0006008
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 11.552, df = 4, p-value = 0.02101
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 9.536, df = 4, p-value = 0.04901
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 37.618, df = 5, p-value = 4.501e-07
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 33.974, df = 13, p-value = 0.001215
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 6.7876, df = 1, p-value = 0.00918
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 10.068, df = 4, p-value = 0.03929
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 11.336, df = 4, p-value = 0.02303
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 49.661, df = 5, p-value = 1.626e-09
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 34.866, df = 13, p-value = 0.0008878
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.18699, df = 1, p-value = 0.6654
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 14.373, df = 4, p-value = 0.006195
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 10.332, df = 4, p-value = 0.03519
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 50.528, df = 5, p-value = 1.081e-09
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 34.641, df = 13, p-value = 0.0009611
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 14.662, df = 1, p-value = 0.0001286
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 12.049, df = 4, p-value = 0.01699
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 15.089, df = 4, p-value = 0.004519
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 54.746, df = 5, p-value = 1.472e-10
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 36.335, df = 13, p-value = 0.0005262
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 9.3516, df = 1, p-value = 0.002228
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 20.051, df = 4, p-value = 0.000488
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 11.455, df = 4, p-value = 0.0219
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 51.643, df = 5, p-value = 6.386e-10
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 36.846, df = 13, p-value = 0.000438